Self-Organizing Agent Teams Learn to Reason Together
Collective intelligence depends not only on what team members know, but also on how they organize their work. When the structure of a solution is unknown, useful roles and divisions of labor cannot be specified in advance; teams must learn from experience how to organize reasoning as it unfolds. Human teams routinely adapt this way, while existing AI agent teams rely on fixed protocols, explicit task decomposition, or routing. We introduce Self-Organizing Agent Teams (SAT), fixed teams of AI agents that learn reusable strategies from prior collaborations to organize roles, conversational phases, participation, and information flow. These strategies enable what we call collaborative comput...
| Authors | Aneesh Pappu, Mirac Suzgun, Yongchan Kwon et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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